TextGGUF2025–2026
Moonshot AI · China
Very large Moonshot MoE models for agentic work. K3 (2.8 trillion parameters) was the largest open model at release, with up to 1M tokens of context and image understanding; K2.7-Code is built for programming. On November 4 the weights of K3.1 Pro were opened (2.8 trillion parameters, 104B active, 1M tokens of context, images): a refinement of K3 for programming, 3D and financial research; by the tests of Moonshot itself, Terminal-Bench 4.0 is 65.7%. The weights are in MXFP4, about 1.56 TB, run through vLLM and SGLang, and need a serious cluster.
- Multi-step agents: search, data collection, reports
- In-depth document analysis
- Help for developers
- Sizes
- 16B-A3B – 2.8T-A104B
- Hardware
- from: 1 GPU
Image + text2026
Microsoft · USA
A compact 4B model that operates a computer by itself: it looks at a screenshot and clicks, and when that is more efficient it switches to the command line and edits files with code. Both roles are in one model, with no external orchestrator. On the OSWorld-Verified test (50 steps) it scores 66.0%, which the authors say is the best result among 4B models, and 87.5% on operating-system tasks. It was trained from the intermediate Mage-VL in five stages, with a set of almost 294 thousand synthetic tasks over 6,915 skills. The context for training and running is 32,768 tokens, and the architecture holds up to 262,144. It suits office automation and assistants on your own computer. The figures come from the authors.
- an agent that works in applications through screenshots and the command line
- automating office tasks on your own computer without a cloud
- a prototype assistant that operates the desktop
- Sizes
- 4B
- Hardware
- from: Laptop
TextOllama2024–2026
NVIDIA · USA
NVIDIA models for agents and reasoning, optimized to run fast on its GPUs. Nemotron 3 is a Mamba and MoE hybrid from 4B to 550B; Nano Omni handles video, audio and images (English only). The new Nemotron 3.5 Super VL (120B, 12B active, up to 1M-token context) reads text, images and video up to two minutes, with a focus on documents and agents; the compressed Bolt (74B, 9B active) fits into 24 GB of video memory. Super VL languages: English, Spanish, French, German, Italian, Japanese. Dory 1.0 (4B, up to 256K-token context, English) runs the same block of layers several times, and the number of passes can be changed per task: more passes give more accuracy without a new model and without long reasoning in the text.
- Agents with tool calling
- Reasoning and calculation tasks
- Answers based on long documents
- Sizes
- 4B – 550B-A55B
- Hardware
- from: Laptop
Text analysisRU2026
Liquid AI · USA
Small decision models: from text, an image or a voice clip they answer yes or no, pick an option or give a rating in one pass, without writing text. d1-3B sees images, d1-omni-600M also hears speech up to 30 seconds. Russian is listed. The open d1-2B (text, pictures and video, based on LFM3-VL-2B) and d1-Omni-2B (sound as well) decide in milliseconds, 5.7 ms on an H100 by the authors measurements; besides choosing an answer they pull out text, numbers and dates with their positions and find objects in a picture. It is not a chat model.
- routing requests and tickets to teams
- checking a photo for defects or violations against a list of questions
- routing voice commands
- Sizes
- 600M, 2B and 3B
- Hardware
- from: Laptop
TextRUOllama2025–2026
Liquid AI · USA
Models with a new architecture for on-device use: fast on a regular CPU and on phones. Versions for data extraction, RAG and tools, plus LFM2.5-VL for images and voice LFM2.5-Audio. The largest, LFM2.5-24B-A2B (2.45B active, 131K-token context, Russian in the language list), always reasons before answering and targets fast on-device agents. In October the LFM3 generation came out on a new tri-hybrid architecture: LFM3-1.7B for local agents (256 thousand tokens of context, about 1 GB of memory at 4 bits, pre-trained on 22 trillion tokens), LFM3-VL-2B for images, LFM3-Audio-2B for speech and LFM3-Omni-2B, which understands text, images, video and sound and answers in text and speech. The authors do not recommend it for complex code and multi-step reasoning.
- Offline assistant on a laptop or phone
- Data extraction from documents
- Tool calling in apps
- Sizes
- 230M – 24B-A2B
- Hardware
- from: Laptop
Computer-use agentsGGUF2025–2026
Ant Group (inclusionAI) · China
An Ant Group family for finding elements on screen and completing tasks in phone and computer interfaces. UI-Venus-2 was specifically trained to refuse dangerous actions. In October LBS-27B came out (Apache 2.0, based on Qwen3.8-27B): it learns to pick and zoom into the needed parts of an image on interface markup, and carries this skill over to ordinary questions about pictures.
- Automating actions in mobile apps
- Filling in forms in web interfaces
- UI autotests
- Sizes
- 2B – 72B
- Hardware
- from: Laptop
Text analysisRU2026
vLLM Semantic Router project · international project
An open family of models for an AI gateway: they answer a request with probabilities without generating text. They detect topic and urgency, jailbreak attempts, harmful requests, personal data and whether a tool is needed. Small versions run on a CPU, Vela understands 17 languages including Russian. The new d3 (27B) also reads several images and videos, and d3-turbo is, by the authors' own estimate, 4 points more accurate on text. The newest in the line, d3-ultra, came out on October 24.
- routing requests between models and teams
- checking a request for a jailbreak attempt or harmful content before the main model
- finding personal data with positions in a text
- Sizes
- 0.3B – 27B
- Hardware
- from: Laptop
TextRUGGUF2025–2026
MiniMax · China
Large MoE models with very long context (up to 1M tokens for Text-01 and M3). M3 is multimodal and understands images. In October the open weights of M3.1-Flash came out (about 428B, 23B active, up to 1M tokens of context, text, images and video): a model for programming, agents and computer use, sped up by sparse attention. MiniMax Community License: free for non-commercial use; commercial use needs the caption "Built with MiniMax M3.1-Flash" and a one-time notice to MiniMax, and a separate permission when the product earns $20 million a year or more; military use is prohibited. The benchmark figures come from MiniMax itself. Licenses differ greatly from version to version.
- Analysis of large document archives in a single request
- Agents with tools
- Help for developers
- Sizes
- 230B-A10B – 456B-A46B
- Hardware
- from: Cluster
TextOllama2026
ConwayResearch (Underdog) · not stated
Qwen3.8-27B squeezed to 2 bits into a single 7.9 GB GGUF file instead of 54 GB, with the focus on keeping tool calling intact. By the authors' measurements (their Underdog Bench), tool calling is 88 against 84 for the full model, parallel calls 42 against 35, and on average 96% of the full model's quality across nine tests. It runs on plain llama.cpp and Ollama, thinking mode can be switched on and off, and it understands images with a separate vision file (about 0.6 to 0.9 GB). Language stated on the card: English.
- an agent with tool calling on a home graphics card
- a local coding assistant with functions
- analysing pictures with the vision file attached
- Sizes
- 27B (7.9 GB file)
- Hardware
- from: Laptop
TextOllama2023–2026
DeepSeek · China
DeepSeek's flagship line: from the first 7B/67B to V4-Pro with 1.6 trillion parameters. Closed-model quality under an open MIT license; V4-Flash-Vision-Exp and V4.1-Flash understand images, context up to 1M tokens. The fresh V4.1-Flash-1017 answers shorter and faster than the previous Flash: by the measurements of DeepSeek itself, 77.0 vs 74.2 on DeepSWE v1.1 and 42.3 vs 31.2 on Terminal-Bench 4.0.
- Employee assistant on your own server
- Analysis of long contracts and reports
- Agents that work with tools and APIs
- Sizes
- 7B – 1.6T-A49B
- Hardware
- from: Laptop
Text2026
Meta Superintelligence Labs · USA
Meta's largest open model: 816B parameters, 42B active per token, a 1 million token context. It understands text, images, video and audio and answers in text. Built for programming and long agentic tasks. Trained on data in more than 100 languages. Access to the weights on Hugging Face is granted manually.
- an agent for working with code and long tasks with tool calling
- analyzing screenshots, charts, documents, video and recordings
- generating training data for other models
- Sizes
- 816B-A42B
- Hardware
- from: Cluster
Documents and OCR2026
Tencent · China
One compact model turns a document page, chart, flowchart, geometry figure, audio or video into structured JSON: with block coordinates, tables, formulas, speech transcription and timestamps.
- parsing scans and PDFs with tables and formulas
- turning charts and flowcharts into a table or diagram
- transcribing audio and video with timestamps
- Sizes
- 5B
- Hardware
- from: 1 GPU
Text analysis2026
Shanghai Artificial Intelligence Laboratory · China
A decision model based on Qwen3.5-4B: from text and images it answers all questions about a situation with probabilities in one pass, without generating text. Suits routing requests and rating urgency.
- routing requests to teams
- rating priority and urgency of requests
- checking from a photo that everything is in order
- Sizes
- 4B
- Hardware
- from: Laptop
Medicine2026
NVIDIA · USA
A language model for computed tomography: looks at a whole chest or abdominal CT volume, not separate slices, writes structured reports and answers questions with reasoning. English only.
- a draft CT report for a physician
- yes-or-no answers about a scan with reasoning
- research in medical imaging
- Sizes
- 4B (based on Qwen3.5-4B with a 3D encoder)
- Hardware
- from: 1 GPU
Documents and OCR2026
Tencent · China
Parses a whole document page: turns it into Markdown with LaTeX formulas and HTML tables. The 4B version scores 95.38 on OmniDocBench v1.6. Languages on the card: English and Chinese.
- turning scans and PDFs into Markdown for search and RAG
- recognizing tables and formulas
- parsing reports and scientific papers
- Sizes
- 2B и 4B
- Hardware
- from: Laptop
Computer-use agents2026
Tencent · China
An open agent for operating a desktop: looks at live screenshots, reasons and outputs clicks and keyboard input. You can show a workflow once and the agent carries it over to a new task. An external environment is needed to execute the actions.
- automating work in programs with no API
- repeating a workflow from one shown example
- testing interfaces
- Sizes
- 27B
- Hardware
- from: 1 GPU
Image + text2026
Interfaze · not stated
One model for developers: reads documents and tables, transcribes speech with speaker labels, finds objects in an image and answers strictly by a JSON schema you supply. Inside is a reasoning core and a set of narrow specialist models.
- parsing documents and tables with text coordinates
- transcribing recordings with speaker labels
- extracting data from documents into a given JSON schema
- Sizes
- a model that needs 80 GB of video memory (the core is based on Qwen3.8-27B)
- Hardware
- from: 1 GPU
Computer-use agents2026
Beijing Academy of Artificial Intelligence (BAAI) · China
An agent model of 27B (based on Qwen3.8-27B) for long tasks: it proposes a solution, measures the result, analyzes errors and revises the solution over several rounds. A 262K-token context. Stated as a research model.
- iterative improvement of code and algorithms
- machine learning tasks with a verifiable result
- deep search and gathering information on a topic
- Sizes
- 27B
- Hardware
- from: 1 GPU
Text analysis2026
AutoTrust AI · Singapore
Open decision models: for yes/no, pick-one and rate-0-to-5 questions they answer with probabilities in one pass, and can also reason when needed. Not affiliated with the closed Jev from TypeSafe.
- routing requests to the right team
- checking an agent action before it runs
- rating urgency and tone of messages
- Sizes
- 9B – 35B
- Hardware
- from: 1 GPU
Text analysis2026
Perplexity · USA
A decision model built on Qwen3.8-27B: it picks an option, answers yes or no or gives a rating, and returns calibrated probabilities. It also understands images.
- routing requests to teams
- checking AI answers against sources
- rating tone and urgency of emails
- Sizes
- 27B
- Hardware
- from: 1 GPU
Text analysisOllama2026
Cloudflare · USA
Decision models: they do not write text but read a situation (text, JSON, an image, video frames, and for Clef-Omni also audio) and return in one pass the probability of each answer to typed questions: yes or no, pick one, rate on a scale. The agent gets ready numbers instead of parsing free text.
- sorting requests and tickets into categories with a probability
- checking a document or photo against a list of questions (is everything stated, is there a violation)
- choosing the next step of an agent without parsing a text answer
- Sizes
- 27B, 9B (Clef-flash) and 30B-A3B (Clef-Omni)
- Hardware
- from: Laptop
Image + textOllama2024–2026
OpenBMB (ModelBest and Tsinghua University) · China
Compact vision models that run even on a phone or laptop. Good at reading text in photos and understanding video; version 4.6 is only 1.3B. The fresh 4.7 comes in two sizes, 1B and 35B-A3B, the first MoE model in the family. According to the authors, 35B-A3B runs on an iPhone 17 Pro Max at 13+ tokens per second with 3 GB of memory thanks to their way of loading experts. The figures come from the authors themselves.
- On-device text recognition in photos
- Processing receipts and documents without sending them to the cloud
- Describing photos and video
- Sizes
- 1B – 35B-A3B
- Hardware
- from: Laptop
TextGGUF2025–2026
StepFun · China
StepFun MoE models built for fast, low-cost work: with 196 billion parameters, Step-3.5/3.7-Flash use about 11 billion per token. Compact Step3-VL-10B for images and voice Step-Audio 2 mini are available. The flagship Step 5 Preview (600B, 27B active, up to 1M-token context, text and images as input) is built for agents, programming and finance tasks.
- High-load agents
- Analysis of documents with diagrams and screenshots
- Help for developers
- Sizes
- 8B to 600B
- Hardware
- from: 1 GPU
TextRUOllama2024–2026
Cohere Labs · Canada
Multilingual models from Cohere's research arm, covering 23 to 100+ languages. Tiny Aya (2026, 3.3B) runs on a regular PC, but for non-commercial use only.
- Translation and correspondence in less common languages
- Multilingual chat assistant
- Analysis of images with text (Vision)
- Sizes
- 3.3B – 35B
- Hardware
- from: Laptop
Drawings and CADRUGGUF2026
GreenMap · not disclosed
A set of small models for Russian-language construction drawings: it finds the drawing area, title block and legend, marks up walls and hatching, and pulls sheet data into JSON along with references to standards. A draft that an engineer checks.
- Finding the title block, legend and drawing area on a sheet
- Marking up walls and hatching on a floor plan
- Extracting drawing data into structured JSON
- Sizes
- 3.5M - 4B
- Hardware
- from: Laptop
MedicineGGUF2023–2026
FreedomIntelligence (The Chinese University of Hong Kong, Shenzhen) · China
A large family of medical models: chat, an imaging version, the reasoning HuatuoGPT-o1 and the new HuatuoGPT-3 on Qwen3. Does not replace a doctor; decisions are made by a specialist.
- Draft discharge summaries for a doctor to review
- Searching medical literature
- Hints for doctors when reviewing images (Vision)
- Sizes
- 7B – 72B
- Hardware
- from: Laptop
TextOllama2023–2026
Shanghai AI Laboratory · China
Models from Shanghai AI Laboratory. The early InternLM line is general-purpose; the new Intern-S1/S2 is scientific: it understands formulas, molecules, charts and images.
- Research assistant: papers, formulas, data
- Analysis of scientific and technical documents
- Corporate chat on small models
- Sizes
- 1.8B – about 1T
- Hardware
- from: Laptop
Documents and OCR2026
Jina AI · Germany
Document parsing in a single model: a whole page becomes Markdown - text in correct reading order, tables and formulas in LaTeX. Built on DeepSeek-OCR, with only 0.6B of its 3.4B parameters active.
- Converting scans and PDFs to Markdown
- Recognizing tables and formulas
- Parsing invoices, acts and reports
- Sizes
- 3.4B-A0.6B
- Hardware
- from: 1 GPU
Image + text2026
Apple · USA
Reads long documents differently: it turns pages into downscaled images and expands the full text only for the pages needed to answer. A long document then costs far less context.
- Answering questions over multi-page documents
- Finding the right page in a large report or case file
- Working with scans and PDFs without a separate OCR step
- Sizes
- 9B
- Hardware
- from: 1 GPU
TextGGUF2025–2026
Xiaomi · China
Xiaomi models for reasoning and agents: from the compact MiMo-7B to MiMo-V2.6-Pro with 1.02 trillion parameters. The larger versions understand text, images, video and audio, with a 1M token context. Languages: English and Chinese.
- Logic and calculation tasks
- Agents with tools
- Help for developers
- Sizes
- 7B – 1,02T-A42B
- Hardware
- from: Laptop
Drawings and CAD2026
FusionBrain Lab, Lomonosov Moscow State University and Innopolis University · Russia
It writes CadQuery code from images and a point cloud, then edits its own output over several rounds, comparing the resulting shape with the original. This is a draft model: an engineer checks and signs off the document.
- Recovering a parametric model from photos and a scan of a part
- Step-by-step refinement of a draft CAD model
- Digitising parts that have no drawings
- Sizes
- 2.2B
- Hardware
- from: Laptop
TextRUOllama2024–2026
Cohere · Canada
Business models: document search with source citations, tool calling, many languages. Command A+ (2026) was the first under Apache 2.0, followed by the North line: code, translation and compact vision.
- Knowledge-base answers with source citations
- Agents that work with internal systems
- Translation and correspondence in different languages
- Sizes
- 2.5B – 218B-A25B
- Hardware
- from: Laptop
TextOllama2023–2026
Zhipu AI (Z.ai) · China
One of the oldest Chinese open lines: from ChatGLM-6B to GLM-5.3. Strong at agentic tasks and programming; GLM-5.3-Flash understands images and is released under MIT.
- Corporate chat assistant
- Agents for routine office tasks
- Help for developers
- Sizes
- 1.5B – 744B-A40B
- Hardware
- from: Laptop
TextOllama2026
Meta Superintelligence Labs · USA
An open Meta model for agents on affordable hardware: distilled from the closed Muse Spark, understands text and images, trained on 100+ languages.
- Agents with tool calling
- Analysis of screenshots, charts and documents
- Multilingual assistant
- Sizes
- 30B
- Hardware
- from: 1 GPU
Autonomous driving2025–2026
NVIDIA · USA
Vision-language-action models for self-driving vehicles: they plan a trajectory from camera video and explain the decision in text. Used to develop and test autopilot systems, not as a ready-made autopilot.
- Auto-labeling camera recordings to train your own driver assistance systems
- Analyzing complex road scenes with text explanations
- Testing autopilot systems in simulation on rare scenarios
- Sizes
- 10B – 34B
- Hardware
- from: 1 GPU
Computer-use agents2025–2026
XLANG Lab (University of Hong Kong) · China
Fully open desktop agents: weights, data and training code. They work on Windows, macOS and Linux; the latest Qwen-CUA controls a computer with ordinary clicks and keystrokes.
- Working in desktop software without an API
- Moving data between systems
- Running user scenarios for tests
- Sizes
- 7B – about 400B (MoE)
- Hardware
- from: 1 GPU
TextRUOllama2023–2026
Alibaba · China
A family of language models with strong Russian language support, from small versions for a laptop to a flagship on par with commercial APIs.
- Chatbot and knowledge-base assistant
- Replies to emails and customer requests
- Document parsing and classification
- Sizes
- 0,6B – 2,4T-A95B
- Hardware
- from: Laptop
Autonomous driving2026
Alibaba (Qwen team) · China
An autonomous driving model based on Qwen3.5-4B: 3D detection of objects around the vehicle, answers to questions about the road scene and trajectory planning in one model.
- A perception and planning prototype for autonomous vehicles on closed sites
- Answering questions about camera recordings when reviewing incidents
- Labeling road scenes to train your own models
- Sizes
- 4B
- Hardware
- from: 1 GPU
TextRUGGUF2025–2026
Swiss AI (ETH Zurich, EPFL, CSCS) · Switzerland
Switzerland's public open model: weights, data and recipe are open, with more than 1000 languages in training. Version 1.5 understands images.
- Multilingual assistant
- Answers based on documents
- Analysis of images and scans (v1.5)
- Sizes
- 0.5B – 70B
- Hardware
- from: Laptop
TextOllama2024–2026
LG AI Research · South Korea
Korean-English models from LG. Most of the line is non-commercial, but the flagship K-EXAONE 2.0 with 750 billion parameters is released under Apache 2.0.
- Corporate assistant
- Working with Korean and English texts
- Analysis of documents and images (4.5)
- Sizes
- 1.2B – 750B-A37B
- Hardware
- from: Laptop
Computer-use agentsGGUF2025–2026
Microsoft · USA
Small Microsoft models for working in the browser: they look at the page and click, type and scroll. Designed to run directly on a work computer without the cloud.
- Filling in web forms and applications
- Collecting data from web portals without an API
- Checking websites against scenarios
- Sizes
- 4B – 27B
- Hardware
- from: Laptop
TextGGUF2026
Thinking Machines Lab · USA
Flagship open models from Mira Murati's lab: they take text, images and audio. Large MoE models that need several GPUs.
- Flagship-level corporate assistant
- Analysis of documents, images and audio
- Programming help
- Sizes
- 276B-A12B, 975B-A41B
- Hardware
- from: Cluster
TextRUOllama2023–2026
Mistral AI · France
European models focused on speed. Mixtral was one of the first open mixture-of-experts models; there are versions for images (Pixtral, Medium 3.5), Lean proofs and moderation (Shieldstral).
- Fast chat responses
- Data extraction from text
- Translation and multilingual work
- Sizes
- 3B – 675B
- Hardware
- from: Laptop
Image + text2026
OpenMOSS (Fudan University) · China
An image + video + text model focused on long videos and precise linking of events to timestamps. A Realtime version handles live video streams.
- Analyzing long videos and finding events by time
- Real-time streaming video analysis
- Understanding photos and documents
- Sizes
- about 11B
- Hardware
- from: 1 GPU
Image + textGGUF2024–2026
Alibaba (AIDC-AI) · China
Vision models from Alibaba's international division with strong text and table reading. The line includes the Ovis2.6 MoE and separate compact OvisOCR models for documents.
- Extracting data from invoices, contracts and delivery notes
- Table recognition
- Answering questions about photos and charts
- Sizes
- 0.9B – 80B-A3B
- Hardware
- from: Laptop
TextOllama2024–2026
Google · USA
Compact Google models that run well on a single computer; larger versions understand images. Includes CodeGemma for code, FunctionGemma 270M for function calling and the fast DiffusionGemma.
- Offline assistant on a laptop
- Reading photos of documents and receipts
- Customer request classification
- Sizes
- 270M – 31B
- Hardware
- from: Laptop
Image + text2023–2026
Shanghai AI Lab (OpenGVLab) · China
A family of video models: encoders for search and classification of clips, and chat models that analyze long videos. InternVideo 3 is designed for multi-hour recordings.
- Searching a video archive with a text query
- Action recognition in video
- Answering questions about a long recording
- Sizes
- small encoders – 9B
- Hardware
- from: Laptop
Image + textOllama2024–2026
Moondream (M87 Labs) · USA
A small, fast vision model for product use cases: answering questions, finding and pointing to objects, captions. Moondream 3.1 is a 9B MoE with 2B active.
- Finding and counting objects in photos
- Checking photos from field reports
- Captions and tags for a catalogue
- Sizes
- 2B – 9B-A2B
- Hardware
- from: Laptop
Computer-use agentsGGUF2025–2026
H Company · France
A French model family for controlling a browser and computer: precisely finds the right element on screen and handles multi-step tasks. The latest Holo3 and 3.1 are open under Apache 2.0.
- Working in web portals and legacy software without an API
- Filling in forms and applications
- Testing interfaces against scenarios
- Sizes
- 0.8B – 235B-A22B
- Hardware
- from: Laptop
Image + textGGUF2025–2026
Kuaishou · China
Vision models from Kuaishou focused on short videos. Keye-VL-2.0 (30B, 3B active) understands well what happens in a clip and when.
- Analysing and describing short videos
- Reviewing clips and content
- Finding the right moment in a video
- Sizes
- 8B – 671B-A37B
- Hardware
- from: Laptop
Image + textOllama2023–2026
LLaVA / LMMs-Lab (researchers from the USA and China) · USA / China
The open project that started the trend for image-plus-text models. The OneVision line understands photos, documents and video; training data and recipes are open.
- Answering questions about photos and screenshots
- Describing products from a photo
- Frame-by-frame video analysis
- Sizes
- 0.5B – 72B
- Hardware
- from: Laptop
Drawings and CADGGUF2026
Autodesk Research · USA
A model from Autodesk Research: from eight rendered views of a part it writes an executable CadQuery program. It was trained on synthetic data only. It produces a draft; an engineer checks and signs off the document.
- Getting a parametric model from a set of views of a part
- Converting finished geometry into editable code
- A draft model for a design office
- Sizes
- 2B (based on Qwen3-VL-2B)
- Hardware
- from: Laptop
TextGGUF2025–2026
ServiceNow · USA
ServiceNow 15B models with step-by-step reasoning that fit on a single GPU. From version 1.5 they also understand images and are good at calling tools.
- A reasoning assistant for internal services
- Tool calling and enterprise agents
- Analysing screenshots and documents with images
- Sizes
- 5B – 15B
- Hardware
- from: Laptop
TextGGUF2025–2026
Baidu · China
Baidu's first open line: from a tiny 0.3B to MoE with 424 billion parameters, including versions that understand images. The mid-size 21B-A3B fits on one GPU; ERNIE-Image 8B draws images with text.
- Corporate assistant
- Analysis of documents and images
- Customer request classification
- Sizes
- 0.3B – 424B-A47B
- Hardware
- from: Laptop
Image + textOllama2025–2026
IBM · USA
Compact IBM models for business documents: tables, charts, forms, field-value pairs. The model card openly warns that it works best with English.
- Extracting fields from forms and invoices
- Turning charts and tables into data
- Answering questions about documents
- Sizes
- 2B – 4B
- Hardware
- from: Laptop
Computer-use agents2024–2026
Show Lab (National University of Singapore) · Singapore
A lightweight model for working with interfaces: finds buttons and fields by description and performs actions on the web and on a phone. ShowUI-π can drag with the mouse.
- Clicking and filling in forms from a task description
- Web UI autotests
- An assistant on a low-end computer without the cloud
- Sizes
- 2B (ShowUI), about 500M (ShowUI-π)
- Hardware
- from: Laptop
MedicineGGUF2025–2026
Zhejiang University · China
A medical model for text, images, 3D scans and video: from a light 4B to a large MoE. Does not replace a doctor; decisions are made by a specialist.
- Hints for doctors when reviewing images and CT scans
- Draft reports and discharge summaries
- Searching medical literature
- Sizes
- 4B – 235B-A22B
- Hardware
- from: Laptop
Image + textRUGGUF2023–2026
Shanghai AI Laboratory (OpenGVLab) · China
A large family of Chinese vision models sized from 1B to 241B. InternVL-U (4B) combines image understanding, generation and editing.
- Understanding documents, diagrams and charts
- Answering questions about photos
- Video analysis
- Sizes
- 1B – 241B-A28B
- Hardware
- from: Laptop
Image + text2024–2026
Ai2 (Allen Institute for AI) · USA
Fully open vision models from Ai2 (weights and data). They can point to a spot in an image and count objects; Molmo2 understands video, MolmoWeb controls a browser.
- Counting products and objects in photos
- Pointing to where an item is in an image
- Video analysis
- Sizes
- 1B-A7B – 72B
- Hardware
- from: Laptop
Drawings and CADGGUF2025–2026
Fudan University and StepFun · China
A family of models that produce SVG both from an image and from a text description, from a simple icon to a detailed illustration. For precise drawings the result has to be checked against the original.
- Vectorising images and symbols into SVG
- Creating icons and pictograms from a text description
- Preparing vector graphics for web and print
- Sizes
- 3B – 8B
- Hardware
- from: 1 GPU
TextRU2025–2026
Reka AI · USA
Compact Reka models: Flash 3 (21B) for reasoning and Reka Edge (7B), which quickly analyzes images and video on-device.
- Photo and video analysis (Edge)
- Object detection in images
- Reasoning tasks (Flash)
- Sizes
- 7B – 21B
- Hardware
- from: Laptop
Computer-use agents2025–2026
Alibaba (Tongyi Lab, X-PLUG) · China
Models for controlling phones and computers from the Mobile-Agent project: they work with Android, Windows, macOS and the browser; version 1.5 has a reasoning mode.
- Automating actions in mobile apps
- Working in desktop software without an API
- Testing apps against scenarios
- Sizes
- 2B – 32B
- Hardware
- from: Laptop
Voice assistantsGGUF2025–2026
OpenBMB (ModelBest, Tsinghua University) · China
A small model that sees, hears and replies by voice in real time, and can clone a voice. Voice dialogue in English and Chinese, text in 30+ languages.
- Voice assistant on your own server
- Analyzing videos and documents
- Voice answers about a camera image
- Sizes
- 8B – 9B
- Hardware
- from: Laptop
Image + text2026
Sukhrob Nurali · not disclosed
A fine-tuned Qwen3-VL-8B reads resume pages as images and returns a 23-field JSON record. The author states plainly that the model is not meant for automated decisions about candidates; a human decides.
- Moving a resume from PDF into a candidate record
- Filling a candidate database without manual typing
- Parsing resumes with different layouts and styling
- Sizes
- 8B, a fine-tune of Qwen3-VL-8B-Instruct
- Hardware
- from: 1 GPU
Medicine2023–2026
Stanford AIMI · USA
Stanford models for chest X-rays: they describe the image and prepare a draft report. Does not replace a doctor; decisions are made by a specialist.
- A draft X-ray description for the radiologist
- Hints for doctors when reviewing images
- Checking reports for completeness
- Sizes
- 3B – 8B
- Hardware
- from: Laptop
Computer-use agentsGGUF2026
Meituan · China
Meituan's computer-control agent, trained on a large number of simulated tasks in desktop software. It outputs clicks and keyboard input.
- Working in office and legacy software without an API
- Moving data between systems
- Running test scenarios
- Sizes
- 8B – 32B
- Hardware
- from: 1 GPU
MedicineGGUF2025–2026
Alibaba DAMO Academy · China
Alibaba's medical model based on Qwen2.5-VL: understands many types of medical images and medical text, and can reason step by step. Does not replace a doctor; decisions are made by a specialist.
- Hints for doctors when reviewing images
- Draft reports and discharge summaries
- Searching medical literature
- Sizes
- 7B – 32B
- Hardware
- from: Laptop
MedicineOllama2025–2026
Google · USA
Google's medical version of Gemma: reads medical texts and images (X-ray, dermatology, histology). A tool for doctors and developers; does not replace a doctor, decisions are made by a specialist.
- Draft discharge summaries and reports for a doctor to review
- Hints for doctors when reviewing images
- Searching and summarising medical literature
- Sizes
- 4B – 27B
- Hardware
- from: Laptop
TextRUOllama2023–2026
Microsoft · USA
Small Microsoft models trained on carefully selected data: strong at logic and math for their modest size. Versions with images and speech are available.
- Assistant on a laptop or your own server
- Reasoning and calculation tasks
- Analysis of images and diagrams (vision versions)
- Sizes
- 1.3B – 42B-A6.6B
- Hardware
- from: Laptop
Computer-use agents2023–2025
Zhipu AI (Z.ai) and Tsinghua University · China
One of the first open models for controlling an interface from a screenshot; its successor, AutoGLM-Phone, works in Android smartphone apps.
- Automating actions in mobile apps
- Working in web interfaces without an API
- Testing apps against scenarios
- Sizes
- 9B – 18B
- Hardware
- from: 1 GPU
Image + text2023–2025
Zhipu AI (Z.ai) and Tsinghua University · China
Vision models from Zhipu: first CogVLM, then the GLM-V line. GLM-4.6V can call tools based on images and act as an agent operating an interface.
- Answering questions about photos and documents
- An agent that operates an interface from screenshots
- Analysing charts and reports
- Sizes
- 9B – 106B-A12B
- Hardware
- from: Laptop
Text2025
Naver · South Korea
Open smaller models from Korea's Naver: from 0.5B to 32B, including reasoning Think versions and multimodal versions that understand images.
- Lightweight Korean-English assistant
- Analysis of images and documents
- Text classification
- Sizes
- 0.5B – 32B
- Hardware
- from: Laptop
Computer-use agentsGGUF2025
Alibaba (Tongyi-MAI) · China
Compact Alibaba models for working in smartphone and computer interfaces: they find elements and complete multi-step tasks. The agent acts on the user's behalf inside their apps, so access rights and limits are set before launch. The follow-up line, Qwen-UI-Agent, was announced in July 2026, but its weights have not been released.
- Automating actions in mobile apps
- Working in software without an API
- UI autotests
- Sizes
- 2B – 8B
- Hardware
- from: Laptop
Drawings and CAD2025
Kingsoft · China
It reads a flowchart from an image and first writes out its structure as JSON - blocks, links and branching conditions - and only then answers a question about the chart. The parse still has to be checked against the original.
- Converting a flowchart image into a JSON structure
- Answering questions about a process diagram
- Digitising procedures and diagrams from presentations
- Sizes
- 32B
- Hardware
- from: 1 GPU
Image + textRU2025
Avito Tech · Russia
Avito's Russian-language model that understands images: describes photos, answers questions about an image, reads text on it. Based on Qwen2.5-VL, faster in Russian than the original.
- Product descriptions from photos in Russian
- Checking that a photo matches its description
- Reading brands and text in images
- Sizes
- 7.4B
- Hardware
- from: 1 GPU
Image + textRUOllama2023–2025
Alibaba (Qwen team) · China
One of the strongest open vision models: reads documents, tables, charts and video, and works with user interfaces. Since Qwen3.5, vision is built directly into the main Qwen model.
- Extracting data from scanned invoices and delivery notes
- Analysing photos of products and shelves
- Analysing video and camera footage
- Sizes
- 2B – 235B-A22B
- Hardware
- from: Laptop
Voice assistantsRUGGUF2025
Alibaba (Qwen) · China
Models that understand text, images, audio and video and reply by voice in real time. Qwen3-Omni speaks 10 languages, including Russian.
- Voice assistant for customers
- Analyzing calls and videos
- Voice answers about documents and images
- Sizes
- 3B – 30B-A3B
- Hardware
- from: Laptop
Plates and vehicles2024–2025
NYU Abu Dhabi (ComNets) · UAE
A model that looks at a photo and answers in words what the plate says: fine-tuned on Malaysian plates, so the Russian format needs fine-tuning. Filming vehicles and people is personal data processing: agree the procedure with a lawyer.
- Reading plates in difficult frames
- Answering questions about a vehicle photo
- Going through an archive of camera photos
- Sizes
- 3B
- Hardware
- from: 1 GPU
Drawings and CAD2025
AIRI, Lomonosov Moscow State University and Innopolis University · Russia
A successor to CAD-Recode: it builds a CAD model as CadQuery code from three kinds of input at once - a point cloud, an image and a text description. It produces a draft; an engineer checks and signs off the document.
- Building a CAD model from a photo of a part
- Recovering a model from a 3D scan
- A draft part from a short text description
- Sizes
- 2.2B (based on Qwen2-VL)
- Hardware
- from: Laptop
Image + textGGUF2025
Moonshot AI · China
An efficient MoE vision model (16B, 3B active) with a long context and a reasoning version. Handles long documents and video well.
- Analysing long PDFs and presentations
- Answering questions about video
- Operating interfaces from screenshots
- Sizes
- 16B-A3B
- Hardware
- from: 1 GPU
Image generationGGUF2025
ByteDance Seed · China
A unified model that understands images, generates them and edits them in a conversation. Similar to how images work in ChatGPT.
- Photo editing in a conversation
- Answering questions about an image
- Image generation with explanations
- Sizes
- 14B-A7B
- Hardware
- from: 1 GPU
TextOllama2023–2025
Meta · USA
The models that started mass open source in AI. A huge ecosystem of fine-tuned versions and tools.
- Assistant for employees
- Summaries of meetings and documents
- Base for industry-specific fine-tuning
- Sizes
- 1B – 405B
- Hardware
- from: Laptop
Image + textGGUF2024–2025
Hugging Face · France / USA
The smallest vision models from Hugging Face, starting at 256M; they run in a browser and on a phone. SmolVLM2 also understands video.
- Describing photos and video on low-end hardware
- Reading simple documents
- Embedding in mobile and offline apps
- Sizes
- 256M – 2.2B
- Hardware
- from: Laptop
Computer-use agentsGGUF2025
ByteDance Seed · China
A model that looks at a screenshot and controls the mouse and keyboard itself: clicks, fills in fields, navigates menus. The first generation and 1.5-7B are open; UI-TARS-2 weights were not released.
- Working in legacy software without an API
- Filling in forms and moving data between systems
- UI autotests from plain-language scenarios
- Sizes
- 2B – 72B
- Hardware
- from: Laptop
Drawings and CADGGUF2025
ServiceNow Research, Mila and ETS Montreal · Canada
It turns a raster image into SVG code: instead of tracing pixels the model writes vector commands, so lines and symbols stay editable. It simplifies complex drawings, so the result needs a visual check.
- Vectorising diagrams, symbols and logos from raster images
- Turning scans of simple drawings into editable vectors
- Recovering icons and pictograms from images
- Sizes
- 1B and 8B
- Hardware
- from: Laptop
Computer-use agentsGGUF2025
Microsoft Research · USA
An agent model that plans actions both in an interface (buttons on screen) and for a robot (arm movements). For now more of a research base than a finished product.
- Pilots in interface control
- Research projects spanning screens and robotics
- Analyzing screenshots with an action plan
- Sizes
- 8B
- Hardware
- from: 1 GPU
Image + textGGUF2024–2025
DeepSeek · China
A single model that both understands images and draws them from a description. Janus-Pro-7B drew attention in early 2025, but its image quality is below specialised models.
- Answering questions about images
- Draft illustrations from a description
- Experiments with a unified vision and generation model
- Sizes
- 1B – 7B
- Hardware
- from: Laptop
Image + text2023–2025
Alibaba DAMO Academy · China
Models that watch a video and answer questions about it: what happens, when, who does what. VideoLLaMA 3 at 2B and 7B is among the strongest in its size class.
- Video description and short summary
- Finding a moment in a recording by question
- Tagging a video archive
- Sizes
- 2B – 72B
- Hardware
- from: Laptop
Image + textGGUF2024
Google · USA
Google's vision model built on Gemma, designed as a base for fine-tuning on a narrow task: captions, object detection, reading text.
- Fine-tuning for your own recognition task
- Finding objects in photos
- Reading text in images
- Sizes
- 3B – 28B
- Hardware
- from: Laptop
Image + textNot maintained2023–2024
Hugging Face · France / USA
Open vision models from Hugging Face that reproduced the closed Flamingo. Idefics3 became the basis for the compact SmolVLM line.
- Answering questions about images
- Analysing documents and screenshots
- A base for fine-tuning
- Sizes
- 8B – 80B
- Hardware
- from: 1 GPU
Documents and OCRNot maintained2024
Microsoft · USA
Turns a scanned page into tagged text with block coordinates, or into markdown. Handy as the first step before parsing a resume. A human makes the decision about a candidate; automatic screening without review must not be used.
- Converting resume scans into text that keeps its structure
- Preparing documents for field extraction
- Digitising paper forms
- Sizes
- about 1.4B
- Hardware
- from: 1 GPU
Image + textNot maintained2024
Microsoft · USA
A very small vision model: captions, object detection, segmentation and text reading from a single prompt. Runs even on a CPU.
- Reading text in photos
- Finding and highlighting objects
- Automatic photo captions
- Sizes
- 0.23B – 0.77B
- Hardware
- from: Laptop
Documents and OCRNot maintained2024
Microsoft · USA
One model for every document task: reading, answering questions about a page, extracting fields, classification. In HR it is used to parse resumes and attached scans. A human makes the decision about a candidate; automatic screening without review must not be used.
- Extracting fields from a resume and its attachments
- Answering questions about document content
- Classifying incoming documents
- Sizes
- 742M
- Hardware
- from: Laptop
MedicineNot maintained2023
Shanghai Jiao Tong University and Shanghai AI Lab · China
An early general-purpose radiology model: understands 2D and 3D images (CT, MRI) together with text. More of a research base. Does not replace a doctor; decisions are made by a specialist.
- Research pilots on CT and MRI analysis
- Hints for doctors when reviewing images
- A base for fine-tuning on the clinic's own images
- Sizes
- size not stated on the model card
- Hardware
- from: 1 GPU
Drawings and CADNot maintained2023
Google Research · USA
Small models that turn an image of a chart or diagram into a table of numbers and answer questions about it. Values read off an image are approximate, so the result has to be checked.
- Turning a chart from a report or PDF into a table
- Natural-language questions about a diagram
- A short text description of a chart
- Sizes
- 282M
- Hardware
- from: Laptop
Image + textNot maintained2023
Google · USA
Reads a document or a screenshot as an image and answers with structure: text, fields, answers to questions. In HR it is fine-tuned for resumes and forms. A human makes the decision about a candidate; automatic screening without review must not be used.
- Extracting data from resumes and forms supplied as images
- Questions about the content of a scan
- Parsing tables and diagrams in documents
- Sizes
- 282M – 1.3B
- Hardware
- from: Laptop
Documents and OCRNot maintained2022
NAVER CLOVA · South Korea
Reads a scanned document and returns a filled-in field structure straight away, with no separate OCR step. In HR it is fine-tuned for parsing resumes and forms. A human makes the decision about a candidate; automatic screening without review must not be used.
- Extracting fields from forms and resumes
- Parsing scans of certificates and diplomas
- Detecting the type of an incoming document
- Sizes
- about 200M
- Hardware
- from: Laptop
